Effects of distance constraints on macromolecular conformation. II. Simulation of experimental results and theoretical predictions

By generating classes of random structures for trypsin inhibitor and carp myogen, each consistent with a given set of experimental or theoretical information, we have assessed the relative utility of various experiments and theories in deducing the conformation of macromolecules. We compare the calculated structures with known x-ray coordinates and compute for each class an average error. Small errors mean that the experimental or theoretical constraints limit the structures to the vicinity of the crystal structure, whereas large errors show that the constraints permit a wide variety of tertiary conformations. We find the following points to hold true: (1) Qualitative information on all the distances, as might be obtained from the correct prediction of interresidue contacts, effectively determines the structure (error approximately 1 Å). (2) Quantitative information on a limited number of distances, as might be obtained from nmr or crosslinking experiments, significantly restricts the range of possible structures only when the number of distances given is comparable to the number of residues (error approximately 3 Å). (3) Quantitative information on the distances of each residue to the center of mass of the molecule, as might in part be obtained from solvent accessibility and solution x-ray studies, is not particularly restrictive by itself (error approximately 5 Å). (4) Complete qualitative local distance information, as might be obtained from secondary prediction and CD/ORD studies, is clearly consistent with a wide variety of tertiary structures (error approximately 7 Å).     

Therapy of Fabry disease with pharmacological chaperones: from in silico predictions to in vitro tests

Background

Fabry disease is a rare disorder caused by a large variety of mutations in the gene encoding lysosomal alpha-galactosidase. Many of these mutations are unique to individual families. Fabry disease can be treated with enzyme replacement therapy, but a promising novel strategy relies on small molecules, so called "pharmacological chaperones", which can be administered orally. Unfortunately only 42% of genotypes respond to pharmacological chaperones.

Results

A procedure to predict which genotypes responsive to pharmacological chaperones in Fabry disease has been recently proposed. The method uses a position-specific substitution matrix to score the mutations. Using this method, we have screened public databases for predictable responsive cases and selected nine representative mutations as yet untested with pharmacological chaperones. Mutant lysosomal alpha galactosidases were produced by site directed mutagenesis and expressed in mammalian cells. Seven out of nine mutations responded to pharmacological chaperones. Nineteen other mutations that were tested with pharmacological chaperones, but were not included in the training of the predictive method, were gathered from literature and analyzed in silico. In this set all five mutations predicted to be positive were responsive to pharmacological chaperones, bringing the percentage of responsive mutations among those predicted to be positive and not used to train the classifier to 86% (12/14). This figure differs significantly from the percentage of responsive cases observed among all the Fabry mutants tested so far.

Conclusions

In this paper we provide experimental support to an "in silico" method designed to predict missense mutations in the gene encoding lysosomal alpha galactosidase responsive to pharmacological chaperones. We demonstrated that responsive mutations can be predicted with a low percentage of false positive cases. Most of the mutations tested to validate the method were described in the literature as associated to classic or mild classic phenotype. The analysis can provide a guideline for the therapy with pharmacological chaperones supported by experimental results obtained in vitro. We are aware that our results were obtained in vitro and cannot be translated straightforwardly into benefit for patients, but need to be validated by clinical trials.

     

Simultaneous capacitance and amperometric measurements of exocytosis: a comparison.

We measured the exocytotic response induced by flash photolysis of caged compounds in isolated mast cells and chromaffin cells. Vesicle fusion was measured by monitoring the cell membrane capacitance. The release of vesicular contents was followed by amperometry. In response to a GTP gamma S stimulus we found that the time integral of the amperometric current could be superimposed on the capacitance trace. This shows that the integrated amperometric signal provides an alternative method of measuring the extent and kinetics of the secretory response. Very different results were obtained when photolysis of caged Ca2+ (DM-nitrophen) was used to stimulate secretion. In mast cells, there was an immediate, graded increase in membrane capacitance that was followed by step increases (indicative of granule fusion). During the initial phase of the capacitance increases, no release of oxidizable secretory products was detected. In chromaffin cells we also observed a considerable delay between increases in capacitance, triggered by uncaging Ca2+, and the release of oxidizable secretory products. Here we demonstrate that there can be large increases in the membrane capacitance of a secretory cell, triggered by flash photolysis of DM-nitrophen, which indicate events that are not due to the fusion of granules containing oxidizable substances. These results show that increases in capacitance that are not resolved as steps cannot be readily interpreted as secretory events unless they are confirmed independently.     

FRET imaging and in silico simulation: analysis of the signaling network of nerve growth factor-induced neuritogenesis

Genetically encoded probes based on Förster resonance energy transfer (FRET) enable us to decipher spatiotemporal information encoded in complex tissues such as the brain. Firstly, this review focuses on FRET probes wherein both the donor and acceptor are fluorescence proteins and are incorporated into a single molecule, i.e. unimolecular probes. Advantages of these probes lie in their easy loading into cells, the simple acquisition of FRET images, and the clear evaluation of data. Next, we introduce our recent study which encompasses FRET imaging and in silico simulation. In nerve growth factor-induced neurite outgrowth in PC12 cells, we found positive and negative signaling feedback loops. We propose that these feedback loops determine neurite-budding sites. We would like to emphasize that it is now time to accelerate crossover research in neuroscience, optics, and computational biology.     

Residential magnetic field measurements in France: comparison of indoor and outdoor measurements.

Residential magnetic field (MF) measurements were performed for the first time in a representative sample of French dwellings. Exposure levels were assessed by two methods: indoor and outdoor measurements. Linear and logistic regression models were used to determine factors associated with the time-weighted average (TWA) home MF. TWA magnetic field magnitudes were approximately log-normally distributed with geometric means under 0.010 microT for both indoor and outdoor measurements. Only 5% of the dwellings presented indoor MF levels greater than 0.120 microT (1.2 mG). Both indoor and outdoor MF variations were explained by three factors: wiring configuration, the dwelling's location (i.e., urban or rural), and housing characteristics (individual houses or apartment building). The reliability of outdoor spot measurements with 30-min bedroom recordings was assessed by an intraclass correlation coefficient. The measurements were accurate in rural areas and small towns. In urban centers, local MF variations spoil the outdoor measurement's reliability. If indoor measurements are taken as the reference method, the use of outdoor instead of indoor measurement leads to an important decrease in statistical power. Further assessment of MF near high power transmission lines is necessary to evaluate the usefulness of outdoor spot recordings near such lines. The urban MF environment also has to be explored to identify extraneous sources.     

An HMM model for coiled-coil domains and a comparison with PSSM-based predictions

MOTIVATION: Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a window-less Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile. RESULTS: We have conceived MARCOIL, an HMM for the recognition of proteins with a CCD on a genomic scale. A cross-validated study suggests that MARCOIL improves predictions compared to the traditional PSSM algorithm, especially for some protein families and for short CCDs. The study was designed to reveal differences inherent in the two methods. Potential confounding factors such as differences in the dimension of parameter space and in the parameter values were avoided by using the same amino acid propensities and by keeping the transition probabilities of the HMM constant during cross-validation. AVAILABILTY: The prediction program and the databases are available at http://www.wehi.edu.au/bioweb/Mauro/Marcoil     

Genetic parameters of milk fatty acid profile in sheep: comparison between gas chromatographic measurements and Fourier-transform IR spectroscopy predictions

Fatty acid (FA) composition is a key component of sheep milk nutritional quality. However, breeding for FA composition in dairy sheep is hampered by the logistic and phenotyping costs. This study was aimed at estimating genetic parameters for sheep milk FA and to test the feasibility of their routine measurement by using Fourier-transform IR (FTIR) spectroscopy. Milk FA composition of 989 Sarda ewes farmed in 48 flocks was measured by gas chromatography (FA_GC). Moreover, FTIR spectrum was collected for each sample, and it was used to predict FA composition (FA_FTIR) by partial least squares regression: 700 ewes were used for estimating model parameters, whereas the remaining 289 animals were used to validate the model. One hundred replicates were performed by randomly assigning animals to estimation and validation data set, respectively. Variance components for both measured and predicted traits were estimated with an animal model that included the fixed effects of parity, days in milking interval, lambing month, province, altitude of flock location, the random effects of flock-test-date and animal genetic additive. Genetic correlations among FA_GC, and between corresponding FA_GC and FA_FTIR were estimated by bivariate analysis. Coefficients of determination between FA_GC and FA_FTIR ranged from moderate (about 0.50 for odd- and branched-chain FA) to high (about 0.90 for de novo FA) values. Low-to-moderate heritabilities were observed for individual FA (ranging from 0.01 to 0.47). The largest value was observed for GC measured C16:0. Low–to-moderate heritabilities were estimated for FA groups. In most of cases, heritabilites were slightly larger for FA_GC than FA_FTIR. Estimates of genetic correlations among FA_GC showed a large variability in magnitude and sign. The genetic correlation between FA_FTIR and FA_GC was higher than 60% for all investigated traits. Results of the present study confirm the existence of genetic variability of the FA composition in sheep and suggest the feasibility of using FA_FTIR as proxies for these traits in large scale breeding programs.     

Solid-phase peptide synthesis (SPPS), C-terminal vs. side-chain anchoring: a reality or a myth

Here we review the strategies for the solid-phase synthesis of peptides starting from the side chain of the C-terminal amino acid. Furthermore, we provide experimental data to support that C-terminal and side-chain syntheses give similar results in terms of purity. However, the stability of the two bonds that anchor the peptide to the polymer may determine the overall yield and this should be considered for the large-scale production of peptides. In addition, resins/linkers which do not subject to side reactions can be preferred for some peptides.     

Making predictions of mangrove deforestation: a comparison of two methods in Kenya

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined.     

Simulation of greenhouse gases following land‐use change to bioenergy crops using the ECOSSE model: a comparison between site measurements and model predictions

This article evaluates the suitability of the ECOSSE model to estimate soil greenhouse gas (GHG) fluxes from short rotation coppice willow (SRC‐Willow), short rotation forestry (SRF‐Scots Pine) and Miscanthus after land‐use change from conventional systems (grassland and arable). We simulate heterotrophic respiration (R_h), nitrous oxide (N₂O) and methane (CH₄) fluxes at four paired sites in the UK and compare them to estimates of R_h derived from the ecosystem respiration estimated from eddy covariance (EC) and R_h estimated from chamber (IRGA) measurements, as well as direct measurements of N₂O and CH₄ fluxes. Significant association between modelled and EC‐derived R_h was found under Miscanthus, with correlation coefficient (r) ranging between 0.54 and 0.70. Association between IRGA‐derived R_h and modelled outputs was statistically significant at the Aberystwyth site (r = 0.64), but not significant at the Lincolnshire site (r = 0.29). At all SRC‐Willow sites, significant association was found between modelled and measurement‐derived R_h (0.44 ≤ r ≤ 0.77); significant error was found only for the EC‐derived R_h at the Lincolnshire site. Significant association and no significant error were also found for SRF‐Scots Pine and perennial grass. For the arable fields, the modelled CO₂ correlated well just with the IRGA‐derived R_h at one site (r = 0.75). No bias in the model was found at any site, regardless of the measurement type used for the model evaluation. Across all land uses, fluxes of CH₄ and N₂O were shown to represent a small proportion of the total GHG balance; these fluxes have been modelled adequately on a monthly time‐step. This study provides confidence in using ECOSSE for predicting the impacts of future land use on GHG balance, at site level as well as at national level.     

Step and ramp induction of myocardial ischemia: comparison of in vivo and in silico results.

This study tested the robustness of our computational model of myocardial metabolism by comparing responses to two different inputs with experimental data obtained in pigs under similar conditions. Accordingly, an abrupt and a gradual reduction in coronary flow of similar magnitude were implemented and used as model input. After flow reductions reached 60% from control values, ischemia was kept constant for 60 min in both groups. Our hypotheses were that: (1) these two flow-reduction profiles would result in different transients (concentrations and flux rates) while having similar steady-state values and (2) our model-simulated responses would predict the experimental results in an anesthetized swine model of myocardial ischemia. The two different ischemia-induction patterns resulted in the same decrease in steady-state MVO2 and in similar steady-state values for metabolite concentrations and flux rates at 60 min of ischemia. While both the simulated and experimental results showed decreased glycogen concentration, accumulation of lactate, and net lactate release with ischemia, the onset of glycogen depletion and the switch to lactate efflux were more rapid in the experiments than in the simulations. This study demonstrates the utility of computer models for predicting experimental outcomes in studies of metabolic regulation under physiological and pathological conditions.     

虚拟环境下距离知觉的加工与整合

距离知觉是判断环境中自我与物体之间的距离或者是物体与物体之间的距离,本文简要介绍了虚拟环境下的距离信息线索的加工以及多感官的整合,系统回顾近年来对距离知觉的研究与探索,并对其进行评价.最后,讨论了运用虚拟现实技术研究距离知觉的一些局限性,并对于未来的研究趋势进行了展望.     

Outer membrane proteome of Actinobacillus pleuropneumoniae: LC-MS/MS analyses validate in silico predictions

The Gram-negative bacterial pathogen Actinobacillus pleuropneumoniae causes porcine pneumonia, a highly infectious respiratory disease that contributes to major economic losses in the swine industry. Outer membrane (OM) proteins play key roles in infection and may be targets for drug and vaccine research. Exploiting the genome sequence of A. pleuropneumoniae serotype 5b, we scanned in silico for proteins predicted to be localized at the cell surface. Five genome scanning programs (Proteome Analyst, PSORT-b, BOMP, Lipo, and LipoP) were run to construct a consensus prediction list of 93 OM proteins in A. pleuropneumoniae. An inventory of predicted OM proteins was complemented by proteomic analyses utilizing gel- and solution-based methods, both coupled to LC-MS/MS. Different protocols were explored to enrich for OM proteins; the most rewarding required sucrose gradient centrifugation followed by membrane washes with sodium bromide and sodium carbonate. This protocol facilitated our identification of 47 OM proteins that represent 50% of the predicted OM proteome, most of which have not been characterized. Our study establishes the first OM proteome of A. pleuropneumoniae.     

KDEL-cargo regulates interactions between proteins involved in COPI vesicle traffic: measurements in living cells using FRET.

How the occupied KDEL receptor ERD2 is sorted into COPI vesicles for Golgi-to-ER transport is largely unknown. Here, interactions between proteins of the COPI transport machinery occurring during a "wave" of transport of a KDEL ligand were studied in living cells. FRET between CFP and YFP fusion proteins was measured by multifocal multiphoton microscopy and bulk-cell spectrofluorimetry. Ligand binding induces oligomerization of ERD2 and recruitment of ARFGAP to the Golgi, where the (ERD2)n/ARFGAP complex interacts with membrane-bound ARF1. During KDEL ligand transport, interactions of ERD2 with beta-COP and p23 decrease and the proteins segregate. Both p24a and p23 interact with ARF1, but only p24 interacts with ARFGAP. These findings suggest a model for how cargo-induced oligomerization of ERD2 regulates its sorting into COPI-coated buds.     

Development and comparison of stream indices of biotic integrity using diatoms vs. non-diatom algae vs. a combination

Stream algal indices of biotic integrity (IBIs) are generally based entirely or largely on diatoms, because non-diatom (“soft”) algae can be difficult to quantify and taxonomically challenging, thus calling into question their practicality and cost-effectiveness for use as bioindicators. Little has been published rigorously evaluating the strengths of diatom vs. soft algae-based indices, or how they compare to indices combining these assemblages. Using a set of ranked evaluation criteria, we compare indices of biotic integrity (IBIs) (developed for southern California streams) that incorporate different combinations of algal assemblages. We split a large dataset into independent “calibration” and “validation” subsets, then used the calibration subset to screen candidate metrics with respect to degree of responsiveness to anthropogenic stress, metric score distributions, and signal-to-noise ratio. The highest-performing metrics were combined into a total of 25 IBIs comprising either single-assemblage metrics (based on either diatoms or soft algae, including cyanobacteria) or combinations of metrics representing the two assemblages (for “hybrid IBIs”). Performance of all IBIs was assessed based on: responsiveness to anthropogenic stress (in terms of surrounding land uses and a composite water-chemistry gradient) using the validation data, and evaluated based on signal-to-noise ratio, metric redundancy, and degree of indifference to natural gradients. Hybrid IBIs performed best overall based on our evaluation. Single-assemblage IBIs ranked lower than hybrids vis-à-vis the abovementioned performance attributes, but may be considered appropriate for routine monitoring applications. Trade-offs inherent in the use of the different algal assemblages, and types of IBI, should be taken into consideration when designing an algae-based stream bioassessment program.     

Prediction vs. reality: Can a PVA model predict population persistence 13 years later?

The challenge of conservation biology is to make models that predict population dynamics and have a high probability of accurately tracking population change (increase, decrease, constancy). In this study we tested whether the transition model is accurate enough to predict population persistence and size 13 years down and whether after 13 years populations had achieved a stable stage distribution. We modeled 6 small populations of an epiphytic orchid using a Lefkovitch type analysis to predict population growth pattern based on monthly surveys for approximately 1.5 years. In addition, sensitivity and elasticity analyses were used to identify life stages with high sensitivity or elasticity that have the largest influence on population growth rate. We re-censused the populations 13 years after the first study and compared the structure of the populations to predictions based on the earlier census data. Effective population growth rates were similar to those expected except for one where the population went extinct. The prediction slightly (but not significantly) overestimated the actual population growth rates of some populations. Elasticity analysis revealed that the adult stage is critical in the life cycle. The observed stage distributions of the populations were not stable at the beginning of the survey and neither were they after 13 years. We suggest that this might be caused by external perturbations that result in unequal mortality between life stages and stochastic recruitment events. The ability of the matrices to predict population size approximately eight generations in the future is encouraging and warrants the continued use of these approaches for PVA.